Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning
Joint Authors
Zhang, Hongjun
Feng, Yuntian
Hao, Wenning
Chen, Gang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-14
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts.
For reinforcement learning, we model the task as a two-step decision process.
Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process.
By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously.
Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction.
On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process.
Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process.
Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process.
Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.
American Psychological Association (APA)
Feng, Yuntian& Zhang, Hongjun& Hao, Wenning& Chen, Gang. 2017. Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141104
Modern Language Association (MLA)
Feng, Yuntian…[et al.]. Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1141104
American Medical Association (AMA)
Feng, Yuntian& Zhang, Hongjun& Hao, Wenning& Chen, Gang. Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141104
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141104